Recovery Improvement by Chemical Additives to Steam Injection: Identifying Underlying Mechanisms Through Core and Visual Experiments
Notice bibliographique
Résumé
Abstract Steam injection of any kind (flooding, cyclic, or gravity drainage) is a proven heavy-oil recovery method; however, it also involves excessive costs due energy and water needed for steam generation. Any effort in reducing this cost or improving oil recovery is essential for sustainable production, especially in times of low oil prices. Chemical additives to steam were suggested a few decades ago to improve two major mechanisms, namely heat transfer and interfacial phenomena, but research in that area discontinued due to the cost and thermal stability problem of the additive chemicals. With recent advancements in nano-technologies, new generation chemicals showed potential to reconsider chemical additives to improve the efficiency of steam injection. This, however, requires extensive research especially for mechanism identification. The objective of this paper is to identify the flow characteristics and the mechanisms involved in recovery enhancement by chemical additives through core and visual tests. To mimic the gravity assisted drainage and flooding type steam displacement tests we performed previously (Bruns and Babadagli 2017) on cores saturated with 27,000 heavy-crude-oil, a visual Hele-Shaw model was designed to simulate the same process and identify the physical characteristics of the steam-condensate-oil interface and the role played by added chemicals. Majority of the chemicals/chemical blends showed either improvement in the rate or ultimate recoveries in the coreflooding tests and, based on this data, the best performing and the most thermally stable chemicals were selected for the visual tests. These chemicals include ionic liquids, internal olefin sulfonate, biodiesel (thermally stable surface active agents) and solvents (heptane), and nano-fluids (silicon oxide). The chemical solution was injected at constant rate and pressure after being vaporized in an oven along with steam and the whole process was recorded with a camera. The contribution to recovery improvement through these phenomena in flooding and gravity controlled cases were identified. Foaming, emulsification, and IFT reduction yielding reduced drag forces between two phases at the interface were observed to be the main reason for positive contribution of chemicals. Biodiesel (Surfactant 1) exhibited a diffusion-like behavior near the injection port where no residual oil was noticed. The solvent (heptane), simulating ES-SAGD, stabilized the flow of steam in the late stage of the experiment due to the viscosity reduction. Improved oil + condensate drainage was assumed to be the contributing mechanism because of the change in surface properties during the injection of the ionic liquid. Nanoparticle, silicon oxide, and the internal olefin sulfonate (Surfactant 2) showed similar improvements in tip-splitting of the displacing fingers. It was concluded that the interfacial tension (IFT) reduction resulted in a wider occupation of the Hele-Shaw cell (better lateral sweep).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».